✈️ Crash Analytics

An Advanced Data-Driven Analysis of Air India Flight AI171 Crash

This project provides a comprehensive analytical breakdown of the tragic Air India Flight AI171 crash using real-world data, statistical modeling, visualizations, and simulated insights. It leverages ADS-B trajectory, casualty data, emergency response metrics, and public reports to explore causes, severity, and survivability.

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📊 Project Highlights

• 🔥 Fatality Rate Modeling — onboard vs ground casualties with Monte Carlo simulations
• ✈️ Flight Profile Reconstruction — climb/descent path using ADS-B-based estimates
• 🧠 Crash Cause Probability — updated using breaking news and Bayesian intuition
• 🧪 Emergency Resource Mapping — breakdown of response team deployments and infrastructure
• 🧬 Survivor Analysis — seat location, hatch escape route, and timeline
• 📉 Comparative Analysis — benchmarking against India’s deadliest aviation incidents
• 🧭 Investigation Scorecard — readiness evaluation based on international coordination and black box recovery

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🔧 Tech Stack

• Python 3.9+
• Pandas – for structured data analysis
• Matplotlib / Seaborn – for beautiful plots
• NumPy – for simulation and numeric work
• (Optional): textblob, plotly, dash – for NLP/sentiment or dashboards

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📁 File Structure

├── Air_India_Crash_Analytics.ipynb       # Base notebook
├── Air_India_Crash_Analytics_Enhanced.ipynb  # Updated version with advanced analysis
├── casualty_metrics.png                  # Key visualizations
├── pilot_experience.png
├── dna_processing_pie.png
├── README.md

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